GridOnClusters: Joint Discretization of Data on a Grid that Preserves Clusters

Discretize continuous multivariate data using a grid that captures the joint distribution via preserving clusters in the original data. Joint grid discretization is applicable as a data transformation step before using other methods to infer association, function, or causality without assuming a parametric model.

Version: 0.0.6
Depends: R (≥ 3.0)
Imports: Rcpp, cluster, fossil, dqrng
LinkingTo: Rcpp
Suggests: Ckmeans.1d.dp, FunChisq, knitr, testthat (≥ 2.1.0), rmarkdown
Published: 2020-03-28
Author: Jiandong Wang [aut], Sajal Kumar ORCID iD [aut], Joe Song ORCID iD [aut, cre]
Maintainer: Joe Song <joemsong at cs.nmsu.edu>
License: LGPL (≥ 3)
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: GridOnClusters results

Downloads:

Reference manual: GridOnClusters.pdf
Vignettes: Examples of joint grid discretization
Package source: GridOnClusters_0.0.6.tar.gz
Windows binaries: r-devel: GridOnClusters_0.0.6.zip, r-devel-gcc8: GridOnClusters_0.0.3.zip, r-release: GridOnClusters_0.0.6.zip, r-oldrel: GridOnClusters_0.0.6.zip
OS X binaries: r-release: GridOnClusters_0.0.6.tgz, r-oldrel: not available
Old sources: GridOnClusters archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=GridOnClusters to link to this page.